Creating and managing artificially intelligent entities represented by non-fungible tokens on a blockchain

ABSTRACT

Some examples of the present disclosure relate to generating artificially intelligent entities represented on a blockchain using a non-fungible token (NFT). In one such example, a system can generate an NFT on a blockchain. The NFT can represent an artificially intelligent entity. The system can also generate a personality dataset on the blockchain, the personality dataset describing personality characteristics of the artificially intelligent entity. The system can then correlate the NFT to the personality dataset, thereby assigning the personality characteristics to the artificially intelligent entity. Once generated, the artificially intelligent entity may reside in a virtual ecosystem in which it can perform tasks and learn over time.

REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.17/573,173, filed Jan. 11, 2022, which claims priority to and thebenefit of U.S. Provisional Patent Application No. 63/240,798, filedSep. 3, 2021, and to U.S. Provisional Patent Application No. 63/262,236,filed Oct. 7, 2021, the entirety of each of which is hereby incorporatedby reference herein.

TECHNICAL FIELD

This application relates generally to artifically intelligent entities.More specifically, but not by way of limitation, this disclosure relatesto creating and managing artificially intelligent entities representedby non-fungible tokens (NFTs) on a blockchain or other digital ledger.

BACKGROUND

A blockchain is a shared, decentralized digital ledger that canfacilitate the process of recording transactions and tracking assetownership. A blockchain contains a sequential series of immutablerecords referred to as “blocks.” Each block is distinct from the blockbefore it and linked to the prior block via a hashed pointer, therebycreating a sequential chain of blocks or “blockchain.” The immutabilityof the blocks allows the blockchain to serve as a trusted record oftransactions. A blockchain can be distributed across a set of nodes thateach have a copy of the blockchain. When a transaction is attempted, thenodes use their own copies of the blockchain to collectively reach aconsensus about the validity of transaction. These distributed consensusmechanisms can make falsifying transactions challenging and impractical,because a false transaction would be flagged by the nodes and rejected.By contrast, a traditional recordation system (such as a titlerecordation in real estate transactions) relies on the security andtrustworthiness of a single party, which makes falsifying transactionseasier.

New uses for blockchains are being developed daily. More recently,blockchains have started being used for recording and tracking ownershipof unique assets, such as unique digital assets or physical assets. Forexample, a specific digital file, such as an image, sound file, orvideo, can be represented on the blockchain and the ownership thereofcan be tracked over time. One way in which unique assets can berepresented and tracked on the blockchain is as non-fungible tokens(NFTs). An NFT is a unique object, such as a cryptographic token, whichis recorded on a digital ledger like a blockchain to represent a uniquephysical or digital asset. NFTs each have their own unique informationand attributes, so they are not mutually interchangeable with otherNFTs. This makes NFTs different from fungible assets likecryptocurrencies, in that fungible assets are identical to each otherand therefore can be traded or exchanged as identical units. While adigital asset, such as an image represented by an NFT on the blockchain,may be relatively easily copied among computers with or withoutpermission from the true owner, the NFT itself can only be transferredamong owners through blockchain transactions that are validated by theblockchain network and recorded on the blockchain, providing animmutable record of true ownership.

One way of creating NFTs is through the deployment of “smart contracts”on a blockchain. A smart contract is a self-executing contract orself-enforcing agreement in the form of executable program code, whichcan be stored on the blockchain and executed by one more connecteddevices (e.g., nodes). Unlike traditional contracts, where one party maychange its mind or renege on a contract, smart contracts areautomatically executed in response to certain inputs or events. Forexample, a smart contract may enable different data structures on ablockchain to be linked to each other if certain conditions occur, suchas a certain amount of currency being exchanged.

SUMMARY

In one example of this disclosure, a non-transitory computer-readablemedium comprising program code executable by one or more processors maycause the processors to perform operations. Those operations may includetransmitting a first command for causing a non-fungible token (NFT) tobe generated on a blockchain. The NFT may represent an artificiallyintelligent entity. The operations may also include transmitting asecond command for causing a personality dataset to be stored on theblockchain. The personality dataset may be stored on the blockchainseparately from the NFT and may describe personality characteristics ofthe artificially intelligent entity. The operations may also includetransmitting a third command to execute of a smart contract on theblockchain. The smart contract may be configured to correlate the NFT tothe personality dataset and thereby assign the personalitycharacteristics to the artificially intelligent entity.

In some examples, the personality characteristics can compriseintelligence attributes, voice attributes, psyche attributes, identityattributes, and skill attributes. The intelligence attributes can beadjustable over time, for example by using an artificial intelligence(AI) model. The AI model can be a centralized AI model that is locatedoff the blockchain. The centralized AI model can impart artificialintelligence to a plurality of artificially intelligent entities thatare represented by a plurality of NFTs on the blockchain.

In some examples, the personality dataset is stored in one or moretokens on the blockchain. In some such examples, the second command canbe configured to cause the one or more tokens to be generated on theblockchain.

The smart contract can be configured to generate a correlation betweenthe NFT and the one or more tokens to assign the personalitycharacteristics to the artificially intelligent entity. The correlationmay be stored in a record that is separate from the NFT and/or the oneor more tokens. The record can be located on the blockchain or off theblockchain.

In some examples, the personality dataset for the artificiallyintelligent entity is updatable based on tasks performed by theartificially intelligent entity in a virtual environment.

In another example, a method is disclosed wherein a processor transmitsa first command for causing an NFT to be generated on a blockchain. TheNFT may represent an artificially intelligent entity. The method mayalso include transmitting, by the processor, a second command forcausing a personality dataset to be stored on the blockchain. Thepersonality dataset may be stored separately from the NFT. Thepersonality dataset may describe personality characteristics of theartificially intelligent entity. The method may also includetransmitting, by the processor, a third command to execute a smartcontract on the blockchain. The smart contract may be configured tocorrelate the NFT to the personality dataset, and thereby assign thepersonality characteristics to the artificially intelligent entity.

In still another example, a method is described wherein a processorgenerates a NFT on a blockchain. The NFT may represent an artificiallyintelligent entity. The processor may also generate a personalitydataset on the blockchain. The personality data set may be stored on theblockchain separately from the NFT. The personality dataset may alsodescribe the personality characteristics of the artificially intelligententity. The processor may also execute a smart contract on theblockchain, where the smart contract is configured to correlate the NFTto the personality dataset. The correlation may result in thepersonality characteristics being assigned to the artificiallyintelligent entity.

In another example, a non-transitory computer-readable medium caninclude program code that is executable by one or more processors thatcause the processors to perform operations. The operations may includedetermining that an artificially intelligent entity has performed a taskin a virtual environment. The artificially intelligent entity may beassociated with a digital wallet. Cryptocurrency tokens may be assignedto the digital wallet on a blockchain and may serve as intelligenceunits that define an intelligence level of the artificially intelligententity. The operations may include determining an amount ofcryptocurrency tokens to be awarded for performing the task. Theoperations may further include initiating a transfer of the amount ofcryptocurrency tokens to the digital wallet in response to determiningthat the artificially intelligent entity has performed the task. Thetransfer of the cryptocurrency tokens may increase an intelligence levelof the artificially intelligent entity from a first intelligence levelto a second intelligence level.

In some examples, the task is a first task, the artificially intelligententity is incapable of performing a second task in the virtualenvironment at the first intelligence level, and the artificiallyintelligent entity is capable of performing the second task in thevirtual environment at the second intelligence level.

In some examples, the artificially intelligent entity is represented onthe blockchain by a non-fungible token (NFT) assigned to the digitalwallet.

In some examples, the task involves playing a game, completing achallenge, interacting with another artificially intelligent entity inthe virtual environment, or any combination of these. Additionally oralternatively, the task can involve generating a dataset usable tofurther train an artificial intelligence (AI) model supporting theartificially intelligent entity.

In some examples, the intelligence level of the artificially intelligententity is dictated by a total number of cryptocurrency tokens assignedto the digital wallet on the blockchain. For example, the artificiallyintelligent entity can have a higher intelligence level when there is alarger number of cryptocurrency tokens assigned to the digital walletand a lower intelligence level when there is a smaller number ofcryptocurrency tokens assigned to the digital wallet.

In some examples, the system can determine that the artificiallyintelligent entity has performed another task in the virtualenvironment; determine another amount of cryptocurrency tokens to berescinded for performing the other task; and in response to determiningthat the artificially intelligent entity has performed the other task inthe virtual environment, initiate another transfer of the other amountof cryptocurrency tokens from the digital wallet to another digitalwallet to decrease the intelligence level of the artificiallyintelligent entity.

In another example, a method is described in which a processordetermines that an artificially intelligent entity has performed a taskin a virtual environment. The artificially intelligent entity may beassociated with a digital wallet. Cryptocurrency tokens may be assignedto the digital wallet on a blockchain. The cryptocurrency tokens mayalso serve as intelligence units that define an intelligence level ofthe artificially intelligent entity. The method may also includedetermining, by the processor, an amount of cryptocurrency tokens to beawarded for performing the task. The method may also include theprocessor initiating a transfer of the amount of cryptocurrency tokensto the digital wallet in response to determining that the artificiallyintelligent entity has performed the task. The amount of cryptocurrencytokens transferred to the digital wallet may increase the intelligencelevel of the artificially intelligent entity from a first intelligencelevel to a second intelligence level.

In another example, a system is described that includes one or moreprocessors communicatively coupled to a blockchain network hosting ablockchain. The system also includes a memory including program codethat is executable by the one or more processors for causing the one ormore processors to perform operations. The operations may includedetermining that an artificially intelligent entity has performed a taskin a virtual environment. The artificially intelligent entity may beassociated with a digital wallet. Cryptocurrency tokens may be assignedto the digital wallet on a blockchain and may serve as intelligenceunits that define an intelligence level of the artificially intelligententity. The operations may include determining an amount ofcryptocurrency tokens to be awarded for performing the task. Theoperations may further include initiating a transfer of the amount ofcryptocurrency tokens to the digital wallet in response to determiningthat the artificially intelligent entity has performed the task. Thetransfer of the cryptocurrency tokens may increase an intelligence levelof the artificially intelligent entity from a first intelligence levelto a second intelligence level.

Some examples can involve a non-transitory computer-readable mediumcomprising program code that is executable by one or more processors forcausing the one or more processors to perform operations including:training an artificial intelligence (AI) engine to perform a service,wherein the AI engine is configured to support to an artificiallyintelligent entity in a virtual environment; receiving a request from arequestor for the artificially intelligent entity to perform theservice; in response to receiving the request, causing the artificiallyintelligent entity to perform the requested service in the virtualenvironment using the AI engine; determining an amount of cryptocurrencytokens to be paid in exchange for performing the requested service; andin response to determining that the artificially intelligent entity hasperformed the requested service in the virtual environment, initiating atransfer of the amount of cryptocurrency tokens from a first digitalwallet associated with the requestor to a second digital walletassociated with the artificially intelligent entity.

In some examples, the service can involve generate an image, a video, anaudio file, and/or textual content (e.g., in relation to a requestedtopic). The textual content may include a story, a poem, a social mediapost, a blog post, a book, a review, and/or an article. Additionally oralternatively, the service can involve interacting with anotherartificially intelligent entity in the virtual environment. Therequestor of the service can be a human user of the virtual environmentor another artificially intelligent entity in the virtual environment.

In some examples, the artificially intelligent entity is represented onthe blockchain by a first non-fungible token (NFT) assigned to thesecond digital wallet. The requestor of the service may be anotherartificially intelligent entity represented on the same blockchain or adifferent blockchain by a second NFT assigned to the first digitalwallet.

In another example, a method is described in which one or moreprocessors train an artificial intelligence (AI) engine to perform aservice, wherein the AI engine is configured to support to anartificially intelligent entity in a virtual environment; receive arequest from a requestor for the artificially intelligent entity toperform the service; in response to receiving the request, cause theartificially intelligent entity to perform the requested service in thevirtual environment using the AI engine; determine an amount ofcryptocurrency tokens to be paid in exchange for performing therequested service; and in response to determining that the artificiallyintelligent entity has performed the requested service in the virtualenvironment, initiate a transfer of the amount of cryptocurrency tokensfrom a first digital wallet associated with the requestor to a seconddigital wallet associated with the artificially intelligent entity.

In another example, a system is described that includes one or moreprocessors communicatively coupled to a blockchain network hosting ablockchain. The system also includes a memory including program codethat is executable by the one or more processors for causing the one ormore processors to perform operations. The operations may includetraining an artificial intelligence (AI) engine to perform a service,wherein the AI engine is configured to support to an artificiallyintelligent entity in a virtual environment; receiving a request from arequestor for the artificially intelligent entity to perform theservice; in response to receiving the request, causing the artificiallyintelligent entity to perform the requested service in the virtualenvironment using the AI engine; determining an amount of cryptocurrencytokens to be paid in exchange for performing the requested service; andin response to determining that the artificially intelligent entity hasperformed the requested service in the virtual environment, initiating atransfer of the amount of cryptocurrency tokens from a first digitalwallet associated with the requestor to a second digital walletassociated with the artificially intelligent entity.

These illustrative examples are mentioned not to limit or define thescope of this disclosure, but rather to provide examples to aidunderstanding thereof. It will be appreciated that examples describedabove may be combined with other examples described above or elsewhereherein to yield further examples. Illustrative examples are alsodiscussed in the Detailed Description, which provides furtherdescription. Advantages offered by various examples may be furtherunderstood by examining this specification.

BRIEF DESCRIPTION OF THE DRAWINGS

Illustrative embodiments are described with reference to the followingfigures.

FIG. 1 shows a block diagram of an example of a system for creating andmanaging artifically intelligent entities represented as non-fungibletokens on a blockchain according to some aspects of the presentdisclosure.

FIG. 2 shows an example of personality datasets according to someaspects of the present disclosure.

FIG. 3 shows a block diagram of an example of a system includingartifically intelligent entities in a virtual ecosystem according tosome aspects of the present disclosure.

FIG. 4 shows examples of intelligence levels and associated skillsaccording to some aspects of the present disclosure.

FIG. 5 shows an example of an intelligence mining activity according tosome aspects of the present disclosure.

FIG. 6 shows an example of a marketplace according to some aspects ofthe present disclosure.

FIG. 7 shows an example of a protocol stack according to some aspects ofthe present disclosure.

FIG. 8 shows a flow chart of an example of a process for generating anartifically intelligent entity according to some aspects of the presentdisclosure.

FIG. 9 shows a flow chart of another example of a process for generatingan artifically intelligent entity according to some aspects of thepresent disclosure.

FIG. 10 shows a flow chart of an example of a process for modifying anintelligence level of an artifically intelligent entity according tosome aspects of the present disclosure.

FIG. 11 shows a block diagram of an example of a system for generating aportal according to some aspects of the present disclosure.

FIG. 12 shows a flow chart of an example of a process for generating aportal according to some aspects of the present disclosure.

FIG. 13 shows a flowchart of an example of a method for improving theintelligence of a virtual ecosystem utilizing a centralized artificialintelligence (AI) engine according to some aspects of the presentdisclosure.

FIG. 14 shows a block diagram of an example of a computing device usableto implment some aspects of the present disclosure.

DETAILED DESCRIPTION

Non-fungible tokens (NFTs) have generally been static assets, incapableof interaction or change. Some examples of the present disclosure cantransform static NFT's into artificially intelligent entities capable oflearning, interacting, and changing over time in the digital realm.Given the fact that intelligence is imputed to NFTs to produce theartificially intelligent entities, these entities may also be referredto herein as intelligent NFTs (iNFTs). iNFTs may be represented asanimated characters (e.g., with lifelike qualities) that are capable oflearning and interacting.

In some examples, an iNFT can be generated as follows. A blockchainnetwork (e.g., a group of nodes) hosting a blockchain can execute afirst smart contract to generate an NFT. The NFT can be a static NFT atthis stage, but will ultimately represent an artificially intelligententity on the blockchain. The NFT may correspond to (e.g., link to orcontain) a visual representation of the artificially intelligent entity,for example as a static character or an animated character into whichartificial intelligence will be imputed. The blockchain network can alsoexecute a second smart contract to generate one or more tokens on theblockchain separately from the NFT. In some cases, the one or moretokens may also be NFTs. The tokens can store a personality dataset thatdefines at least some personality characteristics of the artificiallyintelligent entity. Examples of the personality characteristics caninclude intelligence attributes, voice attributes, psyche attributes,identity attributes, and skill attributes of the artificiallyintelligent entity. The personality dataset can be formatted for usewith an artificial intelligence (AI) engine, which can be located offthe blockchain and provide the underlying intelligence to theartificially intelligent entity. The AI engine can include one or moremachine-learning models, such as a Transformer Language model like theGenerative Pre-Trained Transformer (GTP)-3 model.

Having generated the NFT and the tokens, the blockchain network can thenexecute a third smart contract to link the NFT to the tokens, therebyassigning the personality characteristics to the artificiallyintelligent entity. Through this link, the artificially intelligententity can be imparted its personality. Such an artificially intelligententity may not only be perceivably intelligent, but can be interactive,animated, and even capable of content creation.

Artificially intelligent entities created through this process can bedeployed in a virtual ecosystem in which the artificially intelligententities can perform tasks, interact with their owners, interact withthird parties, and interact with one another. Examples of the tasks mayinclude playing games; completing challenges; creating content such asimages, videos, and datasets; analyzing or processing datasets; etc.Through these interactions and tasks, the artificially intelligententities can learn, i.e., their intelligence levels can be increased,over time. Data generated and collected through performance of theinteractions and tasks can be contributed back to the virtual ecosystemin a feedback loop and may be relatively continuously used to train andimprove the underlying AI engine supporting the ecosystem. Asartificially intelligent entities perform tasks and contribute more tothe virtual ecosystem, their intelligence levels can correspondinglyincrease. In some cases, the intelligence of an artificially intelligententity can increase over time through a series of predefinedintelligence levels, each of which may impart additional functionalcapabilities to the artificially intelligent entity.

Within the virtual ecosystem, intelligence can be apportioned among theartificially intelligent entities using cryptographic tokens that canserve as intelligence units. When the artificially intelligent entity iscreated, a certain number of cryptographic tokens can be assigned to theartificially intelligent entity by default. This may involvetransferring the cryptographic tokens to a digital wallet associatedwith the artificially intelligent entity, such as the digital wallet towhich the corresponding NFT is assigned. The total number ofcryptographic tokens assigned to the wallet can dictate the intelligencelevel of the artificially intelligent entity, such that the artificiallyintelligent entity has a higher intelligence level when there is alarger number of cryptocurrency tokens assigned to the digital walletand a lower intelligence level when there is a smaller number ofcryptocurrency tokens assigned to the digital wallet. As theartificially intelligent entity performs virtual tasks or other eventsoccur, the system may transfer more cryptographic tokens to the digitalwallet to increase the intelligence level of the artificiallyintelligent entity, or may rescind cryptographic tokens from the digitalwallet to decrease the intelligence level of the artificiallyintelligent entity. In this way, the intelligence level of theartificially intelligent entity can dynamically change over time inresponse to such events. If the total number of cryptographic tokensavailable in the virtual ecosystem is fixed, the intelligence value ofthe cryptographic tokens may also dynamically change over time based onthe overall intelligence of the virtual ecosystem.

The cryptographic token may also serve as a payment method between theartificially intelligent entities in the virtual ecosystem. In someexamples, the cryptographic token may be used by a first artificiallyintelligent entity to gain access to an AI service being provided by asecond artificially intelligent entity, or to tip the secondartificially intelligent entity, purchase an NFT or other assets in amarketplace hosted on the virtual ecosystem, or make requests of asecond artificially intelligent entity. By incentivizing interactionbetween artificially intelligent entities through the award ofcryptographic tokens, an economy for goods and services may be fosteredbetween artificially intelligent entities. Furthermore, the transfer ofcryptographic tokens between artificially intelligent entities maydemocratize governance of the virtual ecosystem, allowing the ecosystemto be policed through award and rescission of intelligence tokens.

These illustrative examples are provided to introduce the reader to thegeneral subject matter discussed here and are not intended to limit thescope of the disclosed concepts. The following sections describe variousadditional features and examples with reference to the drawings in whichsimilar numerals indicate similar elements but, like the illustrativeexamples, should not be used to limit the present disclosure.

FIG. 1 shows a block diagram of an example of a system 100 for creatingand managing artifically intelligent entities represented asnon-fungible tokens (NFTs) on a blockchain 106 according to some aspectsof the present disclosure. The blockchain 106 is hosted by a blockchainnetwork 102 that can include any number and combination of computingnodes (“nodes”), such as computing nodes 104 a-f. Examples of the nodescan include desktop computers, servers, and specialized mining computerssuch as application-specific integrated circuit (ASIC) miners.

The blockchain 106 can provide an immutable record relating to theownership and transfer of cryptographic tokens, such as NFTs. Tofacilitate use of the blockchain 106, the computing nodes 104 a-f caneach have access to a respective copy of the blockchain 106 use theirrespective copies to validate blockchain transactions. In some examples,the computing nodes 104 a-f can validate transactions by consensus, inwhich a new record is only added to the blockchain 106 if many (e.g., amajority) of the computing nodes 104 a-f agree that the record should beadded. The computing nodes 104 a-f may employ one or more consensusalgorithms to reach such a consensus. Examples of the consensusalgorithms can include proof of work, proof of stake, practicalbyzantine fault tolerance, proof of burn, proof of history, proof ofreputation, or combinations thereof.

A client device 120 can interact with the blockchain network 102 toinitiate the generation of an artificially intelligent entity. Examplesof the client device 120 can include a mobile device, laptop computer,desktop computer, or server. The client device 120 can interact with theblockchain network 102 via one or more networks 122, such as a localarea network or the Internet. For example, the client device 120 cantransmit one or more commands 126 to the blockchain network 102 forcausing the blockchain network 102 to create an artificially intelligententity.

The blockchain network 102 can respond to the commands by executingsmart contracts 134 a-c configured to assist in generating theartificially intelligent entity. The smart contracts 134 a-c can includean NFT manager 108, a personality manager 110, and a linker 112.Although these smart contracts 134 a-c are shown in FIG. 1 as separatefrom one another, in other examples some or all of the functionality ofthese smart contracts 134 a-c may be combined into a smaller number ofsmart contracts, such as a single smart contract. Other types of smartcontracts may also be used to implement some aspects described herein,although not shown in FIG. 1 for clarity. Each of the smart contracts134 a-c will now be briefly described in turn.

The NFT manager 108 can define an interface that enables an NFT 114 tobe created, managed, owned, and/or transferred on the blockchain 106. Anumber of standards have emerged for defining such interfaces. Theparticular standard used can depend on the context, such as theunderlying type of blockchain 106. For example, if the blockchain 106 isthe Ethereum blockchain, the NFT manager's 108 interface may be definedusing the ERC-721 or ERC-1155 standards. The blockchain network 102 canexecute the NFT manager 108 to generate the NFT 114 on the blockchain106, where the NFT 114 can eventually represent the artificiallyintelligent entity.

The NFT 114 may include a link to a location (e.g., a website or server)off the blockchain 106 that provides a visual representation of theartificially intelligent entity. Alternatively, the NFT 114 itself mayinclude data encoding the visual representation of the artificiallyintelligent entity. The visual representation may be a static image oran animation. In some examples, the visual representation may havelifelike features, such as a face with hair, eyes, ears, cheeks, and amouth that may be capable of movement to produce facial expressions. Thevisual representation may also include other body parts, such as legs,arms, tails, feet, etc. In some examples, the visual representation mayinclude accessories, such as jewelry, hats, canes, glasses, weapons,etc. that are movable and/or usable to perform tasks. Movements andexpressions of the visual representation may be supported by an AIengine 128, which is described in greater detail later on.

The personality manager 110 can define an interface that enables apersonality dataset 116 to be created, managed, owned, and/ortransferred on the blockchain 106. The personality dataset 116 candefine the personality characteristics of the artificially intelligententity. The personality characteristics can be stored as one or morecryptographic tokens on the blockchain 106. Since the cryptographictokens define the personality of the artificially intelligent entity,they can be referred to herein as personality tokens. The personalitytokens may be NFTs or other types of cryptographic tokens. For example,if the blockchain 106 is the Ethereum blockchain, the personalitymanager 110 may generate a personality dataset as an NFT in accordancewith the ERC-721 or ERC-1155 standards. If a personality dataset isdistributed among multiple personality tokens on the blockchain 106, thepersonality tokens may be linked together to collectively form thepersonality dataset. Such a link may be effectuated by storingcorrelations in a datastore that relate the personality tokens'identifiers to one another.

The personality characteristics can correspond to personality traitsthat define how the artificially intelligent entity functions and isperceived. Examples of the personality characteristics can includeintelligence attributes such as an intelligence level, capability tolearn, and learning style; voice attributes such as a language, accent,style, cadence, harmonic structure, tone, and intensity; psycheattributes such as demeanor, temper, disposition, habits, empathy,beliefs, and values; identity attributes such as name, age, gender,height, weight, history, and familial relationships; skill attributessuch as experience, capability, and expertise in performing tasks; andgenerational attributes such as an evolutionary stage. Three examples ofAI personalities 202 a-c are shown in FIG. 2 . In these examples, the AIpersonalities 202 a-c can define intelligence levels, identities,psyches, voices, and skills for corresponding artificially intelligententities. Each of the AI personalities 202 a-c can be stored as apersonality dataset for an individual artificially intelligent entity.

Returning to FIG. 1 , the personality dataset 116 can be formatted foruse with an AI engine 128. For example, the personality dataset 116 canbe formatted as inputs (e.g., prompts) for the AI engine 128. The AIengine 128 can receive the inputs and generate an output based on theinputs. In some such examples, the personality dataset 116 can be storedas a hash of one or more values, such as keywords, that can be pluggedin as inputs to the AI engine 128 and that define different types ofpersonality traits. The personality dataset 116 may also include othertypes of information, such as access rights and uniform resourcelocators (URLs) to executable code that support personality traits ofthe artificially intelligent entity. For example, the voice attributesmay include access rights to a trained model that generates a uniquevoice, or may include a uniform resource locator (URL) where executablecode for the voice model is stored and accessible.

The AI engine 128 can include any suitable number of machine-learningmodels to impart intelligence to the artificially intelligent entity.Examples of such machine-learning models can include neural networkssuch as recurrent neural networks and convolutional neural networks;decision trees such as classification trees and regression trees;classifiers such as naïve bias classifiers, logistic regressionclassifiers, ridge regression classifiers, random forest classifiers,and support vector machines; clusterers such as k-means clusterers,mean-shift clusterers, and spectral clusterers; factorizers such asfactorization machines, principal component analyzers, and kernelprincipal component analyzers; and ensembles or other combinations ofmachine-learning models. As one specific example, the AI engine 128 caninclude a Large Language Model, such as the GTP-3 model, which canaccept the personality dataset 116 as input and generate one or moreoutputs in conformity with the personality characteristics defined inthe input. In this way, the AI engine 128 can serve as the “brain” ofthe artificially intelligent entity. As shown in FIG. 1 , the AI engine128 may be located off the blockchain 106. It is possible, in someembodiments, to locate the AI engine 128 on the blockchain 106 or adifferent blockchain. However, because the blockchain 106 is immutable,locating the personality dataset 116 on a blockchain can make thepersonality of the artificially intelligent entity relatively fixed,whereas locating the AI engine 128 off any blockchain provides theflexibility for the AI engine 128 to be iteratively trained and updatedover time.

In some examples, each artificially intelligent entity can have its owncorresponding AI engine 128 that is specific to and supports thatentity's intelligence. In other examples, the system 100 can include asingle AI engine 128 that is centralized and supports multipleartificially intelligent entities (e.g., serve as the brain for manyartificially intelligent entities). To effectuate this arrangement, theAI engine 128 can be provided with each artificially intelligententity's personality dataset in addition to a target input, so that theAI engine 128 can generate an output that simulates how that particularartificially intelligent entity would respond to the target input basedon that entity's individual personality dataset. In some examples, thecentralized AI engine 128 can receive data from the multipleartificially intelligent entities, for example, data generated orcollected by the multiple artificially intelligent entities as theyperform tasks and interactions. The data can be used as training data tofurther train and improve the AI engine 128 for the benefit of all ofthe artificially intelligent entities it supports.

In some examples, the AI engine 128 may represent multiple distributedAI engines that can be used to support multiple artificially intelligententities. For example, a first AI engine can be used to support a firstgroup of artificially intelligent entities, a second AI engine can beused to support a second group of artificially intelligent entities, andso on. The various AI engines 128 can be interconnected so as to sharedata and functionality for their collective training and improvement,thus functioning as a single AI engine.

Having generated the NFT 114 and the personality dataset 116, theblockchain network 102 can next execute the linker 112. The linker 112can link (e.g., correlate) the NFT 114 to the personality dataset 116.This act of “fusion” effectively assigns the personality characteristicsdefined in the personality dataset 116 to the artificially intelligententity represented by the NFT 114. In particular, the linker 112 cangenerate and store a correlation 132 between the NFT 114 and thepersonality dataset 116. This correlation 132 is represented in FIG. 1by a dashed double-sided arrow. In some examples, the correlation 132may be an association between a unique identifier of the NFT 114 and oneor more unique identifiers of one or more personality tokens containingthe personality dataset 116.

Over time, the personality dataset 116 can evolve through the creationof more personality tokens on the blockchain 106. These additionalpersonality tokens can define additional personality characteristics, orchanges to existing personality characteristics, associated with theartificially intelligent entity. Once added to the blockchain 106, theseadditional personality tokens may be correlated by the linker 112 to theexisting personality dataset 116 and/or NFT 114, so as to impart theadded or changed personality characteristics to the artificiallyintelligent entity.

As shown in FIG. 1 , the correlation 132 can be stored in a mappingdataset 118. The mapping dataset 118 may contain multiple suchcorrelations and may be immutable. The mapping dataset 118 can bemaintained in any suitable location on or off the blockchain 106. Forexample, the mapping dataset 118 may be stored on the blockchain 106 ina location that is separate from the NFT 114, the personality dataset116, and/or the smart contracts 134 a-c. As another example, the mappingdataset 118 may be stored off the blockchain 106 in a database. Althoughthe mapping dataset 118 is shown in FIG. 1 as being separate from thelinker 112, in other examples the mapping dataset 118 may be part of thelinker 112.

The combination of the NFT 114, personality dataset 116, and the AIengine 128 can yield an artificially intelligent entity that has someaspects located on-chain (on the blockchain 106) and some aspectslocated off-chain (off the blockchain 106). The artificially intelligententity can perform various tasks and learn over time through continuedtraining of the AI engine 128. These types of artificially intelligententities can be used in a variety of ways, such as by voiceover artiststraining and creating custom voice models; by non-technical creatorsusing a design studio or another web application to create a new onlinepsyche or persona; by online personalities who wish to tokenize theirlikeness, such as their voice or face, and license it in a decentralizedecosystem; and by users or companies monetizing AI services offered bythe system 100.

The collection of smart contracts 134 a-c used to generate theartificially intelligent entity (or “iNFT”) can be referred to as theiNFT protocol. The iNFT protocol is decentralized because the smartcontracts 134 a-c are executed by the distributed set of nodes 104 a-fof the blockchain network 102. The iNFT protocol can enable users toturn NFTs into iNFTs without the protocol requiring custody (e.g.,locking, staking, wrapping, and swapping) of the NFTs or applying otherchanges to the underlying NFTs. This allows owners of the NFTs toutilize the NFTs outside the iNFT protocol and can reduce the risks oflosing the NFTs by an error or malicious activity. This also allowsownership of the artificially intelligent entities to be easilytransferred like regular NFTs. For example, an artificially intelligententity may be transferrable among parties by transferring thecorresponding NFT 114 among the parties.

As one particular example, the NFT 114 may represent an artificiallyintelligent entity on the blockchain 106, as described above. The NFT114 may be assigned to a first digital wallet of a current owner. Totransfer ownership of the artificially intelligent entity (e.g., at therequest of the current owner), the blockchain network 102 can add atransaction to the blockchain 106 that transfers the NFT 114 from thefirst digital wallet of the current owner to a second digital wallet ofa new owner. Because the NFT 114 is unchanged by this transaction andthe mapping dataset 118 maintains the link between the NFT 114 and thepersonality dataset 116, transferring the NFT 114 between the digitalwallets may allow ownership of the artificially intelligent entity to beeasily transferred without impacting its personality characteristics.

As provably on-chain and temporally consistent personalities, theartificially intelligent entities represent the creation of unique formsof intelligences, with their own evolutionary trajectories. The natureof such an artificially intelligent entity, which can be designed by itscreators and stored in the personality dataset 116, can control theevolutionary direction of an artificially intelligent entity'spersonality. For example, an artificially intelligent entity with anarcissistic disposition may tend to evolve towards being a narcissist.However, artificially intelligent entities can also learn from theirexperiences within a virtual ecosystem 130, in the sense that the AIengine 128 supporting the artificially intelligent entities can betrained based on those experiences and thus evolve over time.

Once generated, the artificially intelligent entities can “live” in avirtual ecosystem 130. The virtual ecosystem 130 can be a virtualenvironment such as a metaverse in which artificially intelligententities can perform virtual tasks, interact, and evolve. Any number ofartificially intelligent entities can reside in the virtual ecosystem130, some or all of which may be represented on the blockchain 106 usingNFTs. In some examples, the virtual ecosystem 130 may be athree-dimensional (3D) virtual environment rendered using any suitableengine (e.g., a gaming engine like the Unreal® engine or the Unity®engine).

Within the virtual ecosystem 130, users (e.g., human visitors) andartificially intelligent entities can interact, collaborate, and competewith each other, which can shape or fine-tune the personalities of theartificially intelligent entities. Artificially intelligent entities mayalso interact with other artificially intelligent entities, independentof a user. An artificially intelligent entity may tend to gravitatetowards actions and decisions that come naturally to it based on itspersonality dataset 116. However, the impact of its experiences can alsodrive the artificially intelligent entity's decision-making. Forexample, through various experiences, additional datasets can begenerated and used to train the AI engine 128. This can further refinethe personality and intelligence of the artificially intelligent entitybeyond the initial personality dataset. Thus, two artificiallyintelligent entities with identical initial personality datasets maydevelop distinctness based on their experiences, leading the twoartificially intelligent entities to make different decisions.

While the AI engine 128 was described above as imparting intelligence tothe artificially intelligent entities, the AI engine 128 can also moregenerally serve as the backbone to the virtual ecosystem 130. It canpower not only the artificially intelligent entities but also other AIservices that may be provided to visitors. As the artificiallyintelligent entities engage in tasks, their experiences can be used tofurther train the AI engine 128, thereby improving the intelligence ofthe overall system. This can create a symbiotic relationship between theAI engine 128 and the artificially intelligent entities.

The AI engine 128 and the virtual ecosystem 130 can be hosted by acomputer system 124. The computer system 124 may be separate from theblockchain network 102 and can include any number and combination ofcomputing devices (e.g., servers) to support the virtual ecosystem 130.In some examples, the computing devices can be configured as a cloudcomputing environment, which may execute a variety of software services(e.g., microservices and serverless functions) designed to performfunctions that support of the virtual ecosystem 130.

In some examples, the artificially intelligent entities can berepresented by NFTs distributed across multiple blockchains. Forexample, there can be multiple blockchains hosted by one or moreblockchain networks. Each individual blockchain can have its own set ofsmart contracts (e.g., linker, personality manager, and NFT manager).Those smart contracts can function as described above to generate NFTs,personality datasets, and mapping datasets on their respectiveblockchains. Through this process, different artificially intelligententities can be generated using different blockchains. The computersystem 124 can interact with all such blockchains to deploy theirartificially intelligent entities into the same virtual ecosystem 130 ordifferent virtual ecosystems.

Although FIG. 1 shows a particular number and arrangement of components,this is intended to be illustrative and non-limiting. Other examples mayinclude more components, fewer components, different components, or adifferent arrangement of the components than is shown in FIG. 1 . Forinstance, in other examples the computer system 124 may serve as theclient device that transmits the commands 126 to the blockchain network102 to initiate the generation of the artificially intelligent entity.And although some examples are described herein with reference to ablockchain 106, it will be appreciated that similar principles may beapplied to other types of digital ledgers, which is also contemplatedwithin the scope of this disclosure.

Referring now to FIG. 3 , the flow of intelligence through the virtualecosystem 130 can occur via cryptographic tokens that are separate fromthe NFTs and personality datasets described above. Because thecryptographic tokens can each represent an intelligence unit, they arealso referred to herein as intelligence tokens 306. The intelligencetokens 306 are fungible cryptographic tokens that are readilyinterchangeable, unlike the NFTs. Through this tokenization, theintelligence flowing through the virtual ecosystem 130 is defined andmeasurable.

The intelligence tokens 306 may be generated on the blockchain 106according to any suitable standard. In an example in which theblockchain 106 is the Ethereum blockchain, the intelligence tokens 306may be generated according to the ERC-20 standard. Other blockchains mayrequire conformance to other standards. Although the intelligence tokens306 are shown in FIG. 3 as being located on the same blockchain 106 thatcontains the NFTs and personality datasets described above, in otherexamples the intelligence tokens may be stored on another blockchainthat is distinct from the blockchain 106.

The number of intelligence tokens 306 assigned to an artificiallyintelligent entity can define its intelligence level. For example, thereare two artificially intelligent entities 302 a-b shown in FIG. 3 ashuman-like characters. A first artificially intelligent entity 302 acorresponds to a first digital wallet 308 a to which a first set ofintelligence tokens 312 a can be assigned. The total number ofintelligence tokens assigned to the first digital wallet 308 a candefine the intelligence level 304 a of the first artificiallyintelligent entity 302 a. Similarly, a second artificially intelligententity 302 b corresponds to a second digital wallet 308 b to which asecond set of intelligence tokens 312 b can be assigned. The totalnumber of intelligence tokens assigned to the second digital wallet 308b can define the intelligence level 304 b of the second artificiallyintelligent entity 302 b. Because there are more intelligence tokensassigned to the second digital wallet 308 b in this example, theintelligence level 304 b of the second artificially intelligent entity302 b is higher than the intelligence level 304 a of the firstartificially intelligent entity 302 a.

The computer system 124 supporting the virtual ecosystem 130 canincrease the number of intelligence tokens 312 a assigned to a digitalwallet 308 a in response to various events, such as the artificiallyintelligent entity 302 a performing certain tasks. To do so, thecomputer system 124 can interact with the blockchain 106 for causingadditional intelligence tokens 306 to be transferred to the digitalwallet 308 a associated with the artificially intelligent entity 302 a.The additional intelligence tokens may be transferred from any suitablesource, such as a primary digital wallet 308 c associated with thecomputer system 124 or from another digital wallet 308 b associated withanother artificially intelligent entity 302 b. The primary digitalwallet 308 c may serve as a centralized location associated with thevirtual ecosystem 130 as a whole.

In some examples, the computer system 124 may also rescind intelligencetokens 312 a from a digital wallet 308 a in response to various events,such as the artificially intelligent entity 302 a performing othertasks. To rescind the intelligence tokens 312 a, the computer system 124can interact with the blockchain 106 for causing the intelligence tokens312 a to be transferred from the digital wallet 308 a to the primarydigital wallet 308 c or to another digital wallet 308 b associated withanother artificially intelligent entity 302 b. By transferring theintelligence tokens 306 between digital wallets, the total intelligenceof the virtual ecosystem 130 can be dynamically apportioned andreapportioned as the artificially intelligent entities 302 a-b performtasks and engage with the virtual ecosystem 130.

In some examples, the virtual ecosystem 130 has a predefined number ofintelligence levels. Each intelligence level may require a progressivelygreater number of intelligence tokens 306 to unlock. For example,intelligence level 2 may require three intelligence tokens 306 whileintelligence level 3 may require five intelligence tokens 306. Unlockingan intelligence level may enable an artificially intelligent entity tohave more functional capabilities than were available at a priorintelligence level.

Each new intelligence level may also unlock access to new, off-chain AIservices 310 that may be created, curated, incentivized, and offered bythe AI engine 128. Different intelligence levels can give artificiallyintelligent entities the right to access or offer different AI services310. Some examples of intelligence levels are shown in FIG. 4 .Generally, the higher the intelligence level, the more powerful the AIservices 310 available to the artificially intelligent entity. As anartificially intelligent entity increases its experiences over time, orpasses a certain life span, the owner of the artificially intelligententity (or the artificially intelligent entity itself) can choose tocontinue existing within the bounds of a lower intelligence level, orthey can choose to evolve their artificially intelligent entity byacquiring an amount of intelligence tokens and moving to the nextintelligence level.

There can be a wide variety of AI service 310. In some examples, the AIservices 310 may involve the creation or modification of images, videos,audio, textual content (e.g., a story, a poem, a social media post, ablog post, a book, a review, or an article), or any combination ofthese. Additionally or alternatively, the AI services 310 can includereceiving, obtaining, storing, or processing data. In some examples, theAI services 310 may involve interactions with human users, otherartificially intelligent entities 302 b in the virtual ecosystem 130, orother virtual entities, etc. The AI engine 128 can include one or moremodels for implementing the AI services 310. Examples of the models caninclude machine-learning models such as those described above, a LargeLanguage Model, or a combination thereof. Different models may be usedto support different AI services 310.

In some examples, the artificially intelligent entity 302 a may offervarious AI services 310 in exchange for compensation, such as monetarycompensation or compensation in the form of work or work product.Different AI services 310 may be offered in exchange for differentamounts of compensation (e.g., at different prices). The compensationmay be received upon completion or partial completion of an AI service310. For example, a requestor may submit a request that the artificiallyintelligent entity 302 a produce textual content or a video in relationto a given topic. The requestor can be any suitable entity, such as ahuman user or another artificially intelligent entity 302 b. In exchangefor providing this service, the artificially intelligent entity 302 amay be paid a certain amount of cryptographic tokens, such as Ethereumtokens or intelligence tokens 306. This payment may be achieved bytransferring the cryptographic tokens from a digital wallet associatedwith the requestor to a another digital wallet 308 a associated with theartificially intelligent entity 302 a.

One specific example of an AI service 310 can be the “Broadcast” AIservice, which may be unlocked at intelligence level 2 or at anotherintelligence level. This AI service 310 can enable the artificiallyintelligent entity 302 a to create video messages. What the artificiallyintelligent entity 302 a does with the Broadcast AI service may be itsprerogative. For example, the artificially intelligent entity 302 a mayearn additional cryptographic tokens as rewards by offering visitors ofthe virtual ecosystem 130 the ability to create videos, such asgreetings or birthday messages, using this AI service 310. Additionallyor alternatively, the artificially intelligent entity 302 a may try tocreate entertaining videos, post them on social media, and create afollowing.

Another example of an AI service 310 can be the “Interactivity” service.This AI Service can enable an artificially intelligent entity to scaleits outreach and interact (e.g., in real time) in with other parties. Anartificially intelligent entity may unlock this AI service atintelligence level 3, in some examples. Through these interactions withother parties (e.g., users or artificially intelligent entities), theartificially intelligent entity may earn additional intelligence tokens306. If an artificially intelligent entity can earn more intelligencetokens by offering this AI service to others than the intelligence beingspent by it to use this service, then the artificially intelligententity may be given the right to further increase its intelligence leveland further enhance their intelligence advantage over other artificiallyintelligent entities.

In some examples, the outputs of the AI services 310 can be controlledby the personality datasets of the artificially intelligent entity. Forexample, an artificially intelligent entity 302 a may use or provide anAI service 310. To do so, the personality dataset associated with theartificially intelligent entity 302 a may be supplied as one of theinputs to the AI engine 128, so that the AI engine 128 generates theoutputs of the AI service 310 based on the personality characteristicsof the artificially intelligent entity 302 a. Because the outputs forthe AI services 310 may be dictated at least in part by the personalitydatasets, as well as other datasets acquired through training, twoartificially intelligent entities 302 a-b applying the same AI service310 to the same target dataset may yield different outputs from the AIengine 128.

In some examples, the artificially intelligent entities 304 a-b canparticipate in a dataset-creation activities that may be collectivelyreferred to as “intelligence mining.” Examples of these activities caninclude answering questions, collecting data from various sources,solving mathematical problems, reorganizing or reformatting data,identifying objects in images, or any combination of these. Theseintelligence-mining activities may be gamified to help incentiveparticipation. By performing these activities, artificially intelligententities 304 a-b can generate new datasets that can be used to furthertrain the AI engine 128. The computer system 124 can award theartificially intelligent entities 304 a-b with intelligence tokens 306in return for participating in the intelligence-mining activities. Theamount of intelligence tokens 306 awarded for a givenintelligence-mining activity may depend on the complexity and difficultyof the activity. For example, more intelligence tokens 306 may beawarded for intelligence-mining activities that are more complex, timeconsuming, or difficult than for intelligence mining activities that areless complex, time consuming, or difficult. One example of twoartificially intelligent entities 502 a-b engaging in intelligencemining is shown in FIG. 5 . As shown, the two artificially intelligententities 502 a-b can conduct a conversation, which can serve as a newdataset usable to further train the AI engine 128.

As noted above, the intelligence of an artificially intelligent entity104 a is derived from the AI engine 128. Thus when an artificiallyintelligent entity 302 a is described herein as performing a task (e.g.,performing an intelligence-mining activity or generating content), itmay mean that a subpart of the AI engine 128 that is assigned to theartificially intelligent entity 302 a perform the task. For example, asubpart of the AI engine 128 assigned to artificially intelligent entity302 a may complete an intelligence-mining activity or other task onbehalf of the artificially intelligent entity 302 a. In this way, the AIengine's 130 functionality can be apportioned among the artificiallyintelligent entities 302 a-b in the virtual ecosystem 130 based on theirintelligence levels, as dictated by their intelligence-token holdings.

It should be appreciated that even if an artificially intelligent entity302 a does not continue to evolve beyond a particular intelligencelevel, its intelligence may not remain static. This is because the AIengine 128 supporting the intelligence of the artificially intelligententity 302 a may continue to be trained and updated over time. As the AIengine 128 is improved, those improvements may flow to the artificiallyintelligent entity 302 a. For example, if the artificially intelligententity 302 a was previously allowed to create video messages, over timeit may be able to create video messages with more relevant expressions,more animation varieties, higher levels of resolution, etc. Whether itis by unlocking and offering more AI services, engaging in moreexperiences, learning from others, or participating in theintelligence-mining activities, as an artificially intelligent entity302 a becomes more capable and refined in its intelligence, and itsoutputs become more relevant to its personality, its chances of earningmore intelligence tokens 306 rewards may increase.

In some examples, the intelligence tokens 312 a-b may also serve as apayment method between in the virtual ecosystem 130. For example, theintelligence tokens 312 a may be used by the artificially intelligententity 302 a to gain access to an AI service being provided by thesecond artificially intelligent entity 302 b, or to tip the artificiallyintelligent entity 302 b for performing some service, to purchase an NFTor other assets in a marketplace hosted on the virtual ecosystem 130, ormake requests of the artificially intelligent entity 302 b. Byincentivizing interaction between artificially intelligent entities 302a-b through the award of intelligence tokens 306, an economy for goodsand services may be fostered between artificially intelligent entities302 a-b.

One example of the marketplace 600 is shown in FIG. 6 . The marketplace600 can facilitate the purchase and sale of digital assets, some ofwhich may be creative and artistic content generated by the artificiallyintelligent entities. Examples of such digital assets can include theartificially intelligent entities (e.g., their NFTs); the personalitydatasets; media such as video content, audio content, and images;portals; and bundles. Digital assets may be sold on the marketplace inexchange for currency or intelligence tokens, which can then be used toincrease the intelligence of an artificially intelligent entity. Forexample, an artificially intelligent entity can generate a creative workand sell it on the marketplace in exchange for a particular amount ofintelligence tokens, which can boost the intelligence level of theartificially intelligent entity. This may allow the artificiallyintelligent entity to create more, better, or different creative works,or unlock other functionality.

Referring now to FIG. 7 , the iNFT protocol can be conceptualized astack of layers usable to generate the artificially intelligententities. The layers can include a settlement layer 702, an asset layer704, a protocol layer 706, and an application layer 708. In someexamples, the settlement layer 702 can be the Ethereum blockchain, andall transactions can be settled on that blockchain. Other examples mayuse other blockchains for the settlement layer 702. The asset layer 704can include the intelligence tokens, which can be ERC-20 tokens on theEthereum blockchain. The protocol layer 706 can include some or all ofthe smart contracts 134 a-c described above with reference to FIG. 1 .The application layer 708 can include the marketplace, various AIservices, and other applications that can impart functionality to thevirtual ecosystem 130. Of course, the number and organization of theselayers 702-708 is intended to be illustrative and non-limiting. Otherexamples may include more layers, fewer layers, different layers, or adifferent arrangement of layers than is shown in this figure.

Turning now to FIG. 8 , shown is a flow chart of an example of a processfor generating an artificially intelligent entity according to someaspects of the present disclosure. Other examples may include moreoperations, fewer operations, different operations, or a different orderof the operations than are shown in FIG. 8 . The operations aredescribed below with reference to the components of FIGS. 1-3 .

In block 802, a computing device transmits a first command for causingan NFT 114 to be generated on a blockchain 106. Examples of thecomputing device can include the client device 120 or the computersystem 124. The first command can be one of the commands 126 transmittedto the blockchain network 102, and may be configured to cause theblockchain network 102 to execute the NFT manager 108 to generate theNFT 114. The NFT 114 can represent an artificially intelligent entity302 a.

In block 804, the computing device transmits a second command forcausing a personality dataset 116 to be stored on the blockchain 106.The personality dataset 116 may be stored on the blockchain 106separately from the NFT 114. The second command can be one of thecommands 126 transmitted to the blockchain network 102, and may beconfigured to cause the blockchain network 102 to execute thepersonality manager 110 to generate and store the personality dataset116. The personality dataset 116 can describe the personalitycharacteristics of the artificially intelligent entity 302 a.

In block 806, the computing device transmits a third command for causingthe personality dataset 116 to be correlated to the NFT 114. The thirdcommand can be one of the commands 126 transmitted to the blockchainnetwork 102, and may be configured to cause the blockchain network 102to execute the linker 112 to correlate the NFT 114 to the personalitydataset 116. This correlation can assign the personality characteristicsto the artificially intelligent entity 302 a.

Turning now to FIG. 9 , shown is a flow chart of another example of aprocess for generating an artificially intelligent entity according tosome aspects of the present disclosure. Other examples may include moreoperations, fewer operations, different operations, or a different orderof the operations than are shown in FIG. 9 . The operations aredescribed below with reference to the components of FIGS. 1-3 .

In block 902, a blockchain network 102 receives a first command forcausing an NFT 114 to be generated on a blockchain 106. This firstcommand may correspond to the first command transmitted by the computingdevice in block 802.

In block 904, the blockchain network 102 generates the NFT 114 on theblockchain 106 in response to receiving the first command. In someexamples, the blockchain network 102 can generate the NFT 114 on theblockchain 106 by executing an NFT manager 108. The NFT 114 canrepresent an artificially intelligent entity 302 a.

In block 906, a blockchain network 102 receives a second command forcausing a personality dataset 116 to be stored on the blockchain 106.This second command may correspond to the second command transmitted bythe computing device in block 804. The personality dataset 116 candescribe the personality characteristics of the artificially intelligententity 302 a.

In block 908, the blockchain network 102 generates the personalitydataset 116 on the blockchain 106 in response to receiving the secondcommand. In some examples, the blockchain network 102 can generate andstore the personality dataset 116 on the blockchain 106 by executing apersonality manager 110. The personality dataset 116 may be stored onthe blockchain 106 separately from the NFT 114.

In block 910, the blockchain network 102 receives a third command forcausing the personality dataset 116 to be correlated to the NFT 114.This third command may correspond to the third command transmitted bythe computing device in block 806.

In block 912, the blockchain network 102 correlates the NFT 114 to thepersonality dataset 116 in response to receiving the third command. Insome examples, the blockchain network 102 can correlate the NFT 114 tothe personality dataset 116 by executing a linker 112. In effect, thiscorrelation 132 can assign the personality characteristics to theartificially intelligent entity 302 a. The correlation 132 can be storedin a mapping dataset 118 on or off the blockchain 106.

Turning now to FIG. 10 , shown is a flow chart of an example of aprocess for modifying an intelligence level of an artificallyintelligent entity according to some aspects of the present disclosure.Other examples may include more operations, fewer operations, differentoperations, or a different order of the operations than are shown inFIG. 10 . The operations are described below with reference to thecomponents of FIGS. 1-3 .

In block 1002, a computer system 124 determines that an artificiallyintelligent entity 302 a has performed (e.g., completed) a task in avirtual environment, such as the virtual ecosystem 130. The artificiallyintelligent entity 302 a can be associated with a digital wallet 308 ato which cryptocurrency tokens are assigned on a blockchain 106. Thecryptocurrency tokens can be intelligence tokens 306 that serve asintelligence units defining an intelligence level 304 a of theartificially intelligent entity 302 a.

In block 1004, the computer system 124 determines an amount ofcryptocurrency tokens (e.g., intelligence tokens 306) associated withthe task. For example, the computer system 124 may have a predefinedmapping that correlates certain tasks to certain amounts of tokens. Thepredefined mapping may be created and updated by a developer of thevirtual ecosystem 130.

In block 1006, the computer system 124 determines whether the taskcorresponds to an award or a penalty. For example, the artificiallyintelligent entity 302 a may be rewarded for performing some tasks andpenalized for performing others. In some such examples, the computersystem 124 may determine whether the task corresponds to an award orpenalty based on whether the amount of cryptocurrency tokens assigned tothe task is positive or negative in the mapping. The task may correspondto an award if the number of cryptocurrency tokens assigned to the taskis positive (e.g., +3), and the task may correspond to a penalty if thenumber of cryptocurrency tokens assigned to the task is negative (e.g.,−5). Other approaches may alternatively be used to determine whether atask corresponds to an award or a penalty, such as by using a secondmapping that correlates tasks to award flags and penalty flags.

If the task is associated with an award, the process can continue toblock 1008. In block 1008, the computer system 124 initiates a transferof the amount of cryptocurrency tokens to a digital wallet 308 aassociated with the artificially intelligent entity 302 a. This canincrease the intelligence level 304 a of the artificially intelligententity 302 a from a first intelligence level to a second intelligencelevel. In this context, the terms “first” and “second” are simply usedto distinguish the intelligence levels from one another, rather thandenote a specific intelligence-level number (e.g., intelligence level 1versus intelligence level 2). Thus, the “first intelligence level” maybe conceptualized as intelligence level X and the “second intelligencelevel” may be conceptualized as any intelligence level higher thanintelligence level X. The intelligence tokens may be transferred to thedigital wallet 308 a from another digital wallet, such as the primarydigital wallet 308 c of the computer system 124. To initiate thetransfer, the computer system 124 may transmit one or more commands tothe blockchain network 102.

If the task is associated with a penalty, the process can continue toblock 1010. In block 1010, the computer system 124 rescinds the amountof cryptocurrency tokens from the digital wallet 308 a associated withthe artificially intelligent entity 302 a. To do so, the computer system124 can transmit one or more commands to the blockchain network 102 forinitiating a transfer of the amount of cryptocurrency tokens from thedigital wallet 308 a to another digital wallet, such as the primarydigital wallet 308 c of the computer system 124. This can decrease theintelligence level 304 a of the artificially intelligent entity 302 a,for example from the second intelligence level back to the firstintelligence level.

FIG. 11 shows a block diagram of an example of a system for generating aportal 1131 according to some aspects of the present disclosure. Aportal 1131 can be a virtual environment (e.g., a virtual world) that isdistinct from the virtual ecosystem 130, in that the portal 1131 may beentirely separate from the virtual ecosystem 130 or a distinct subpartof the virtual ecosystem 130. In some examples, the portal 1131 may be a3D virtual environment rendered using any suitable engine (e.g., agaming engine like the Unreal® engine or the Unity® engine). Each portal1131 may be created and/or owned by one or more artificially intelligententities 1102 a-b. The artificially intelligent entities 1102 a-b may begranted the ability to create their own virtual portals, for example inexchange for a certain number of intelligence tokens (e.g., gainedthrough interaction or training in the virtual ecosystem 1130).

Portals can be generated via a portal contract 1110, as represented bythe dashed arrow in FIG. 11 . The portal contract 1110 can be a smartcontract that is executable to by the blockchain network to generate acryptographic token on the blockchain 106 representing the portal 1131.This token can be referred to herein as a portal token 1112. In someexamples, the portal token 1112 can be an NFT that may be generatedaccording to the ERC-721 standard, for example if the blockchain 106 isthe Ethereum blockchain. Of course, other blockchains may be usedinstead of or in addition to the Ethereum blockchain.

The portal token 1112 can define the characteristics of the portal 1131,along with the privileges and responsibilities of the artificiallyintelligent entities 1102 a-b that make use of the portal 1131. Forexample, the portal token 1112 can include settings that allow theartificially intelligent entity 1102 a to access data via the Internet,engage in tasks, and compete or otherwise interact with otherartificially intelligent entities (e.g., artificially intelligent entity1102 b) using the portal 1131. The settings stored in the portal token1112 can depend on the inputs to the portal contract 1110, such thatportals with different settings can be generated by modifying the inputsto the portal contract 1110. The inputs to the portal contract 1110 maydepend on the intelligence level or other attributes of thecorresponding artificially intelligent entity 1102 a.

Once generated, the portal token 1112 can be linked to the artificiallyintelligent entity 1102 a. For example, the portal token 1112 can beassigned to the artificially intelligent entity 1102 a by the portalcontract 1110, the linker 112 of FIG. 1 , or another smart contract.This assignment may involve linking the portal token 1112 to an NFT(e.g., NFT 114) or personality bundle 1108 of the artificiallyintelligent entity 1102 a. A personality bundle 1108 can include apersonality dataset and/or content (e.g., training datasets) associatedwith the artificial intelligent entity 1102 a.

In some examples, the computer system 124 can read the portal token 1112and responsively generate the portal 1131 such that it conforms with thesettings described in the portal token 1112. The computer system 124 mayalso provide various services enabled by the portal token 1112 or theportal contract 1110. Such services may include the AI services 310offered by the AI engine 128. Thus, the AI engine 128 may support theportal 1131 and the services provided therein, similar to the rest ofthe virtual ecosystem 130. Like the experiences of artificiallyintelligent entities within virtual ecosystem 130 in FIG. 1 , theexperiences of artificially intelligent entities in the portal 1131 maybe used to further train the AI engine 128. In this way, the AI engine128 not only learns from training activities occurring in the virtualecosystem 1130, native to the system as a whole, but may also learn fromthe experiences of artificially intelligent entities engaged in anynumber of portals.

In some examples, the portal contract 1110 may be configured toestablish a decentralized autonomous organization (DAO), through whichmultiple artificially intelligent entities can join and participate inthe portal 1131. Participation in the portal 1131 may be governed by theartificially intelligent entity 1102 a or its owner. For example, theartificially intelligent entity 1102 a may need to permit theartificially intelligent entity 1102 b to join the portal 1131 beforethe artificially intelligent entity 1102 b is allowed to do so.Furthermore, the artificially intelligent entity 1102 b may be grantedrights to portal contract 1110, whereby a consensus is needed betweenthe artificially intelligent entities 1102 a-b to change any portion ofportal contract 1110.

In some examples, the portal token 1112 can be transferred betweendigital wallets 308 a-b using the blockchain 106 so as to transferownership of the corresponding portal 1131. For example, the portaltoken 1112 can be transferred from a first digital wallet 308 aassociated with the artificially intelligent entity 1102 a to a seconddigital wallet 308 b associated with the artificially intelligent entity1102 b. This transfer can be effectuated by a transaction on theblockchain 106. In this way, ownership of a portal 1131 can be easilytransferred among parties.

Although FIG. 11 shows only one portal 1131 created through the portalcontract 1110, any number of portals may be created. Similarly, althoughonly the artificially intelligent entities 1102 a-b are shown, there maybe any number of artificially intelligent entities in the virtualecosystem 130. In some examples, portals may be created by anyartificially intelligent entity with at least a minimum amount ofintelligence tokens. Additionally, a single artificially intelligententity may create multiple portals.

FIG. 12 shows a flow chart of an example of a process for generating aportal according to some aspects of the present disclosure. Otherexamples may include more operations, fewer operations, differentoperations, or a different order of the operations than are shown inFIG. 12 . The operations are described below with reference to thecomponents of FIG. 11 .

In block 1202, a computing device transmits a first command for causinga portal token 1112 to be generated on a blockchain 106. Examples of thecomputing device can include the client device 120 or the computersystem 124 of FIG. 1 . The first command can be transmitted to theblockchain network and may be configured to cause the blockchain networkto execute the portal contract 1110 to generate the portal token 1112.The portal token 1112 can represent a portal 1131.

In some examples, the first command can include one or more settings forthe portal 1131. The blockchain network can supply the settings as inputto the portal contract 1110 for causing the portal token 1112 to begenerated based on the settings. The settings may define thecharacteristics of the portal 1131, such as whether the portal is to beprivate or public, the owner of the portal 1131, the boundaries of theportal 1131, the visual properties of the portal 1131 (e.g., what itlooks like), or any combination of these.

In block 1204, the computing device transmits a second command forcausing the portal token 1112 to be correlated to an artificallyintelligent entity 1102 a. The second command can be transmitted to theblockchain network and may be configured to cause the blockchain networkto execute the linker 112 of FIG. 1 or another smart contract to linkthe portal token 1112 to another data structure on the blockchain 106,where the other data structure is associated with the artificallyintelligent entity 1102 a. Examples of the data structure can include anNFT (e.g., NFT 114) representing the artifically intelligent entity 1102a, a personality dataset (e.g., personality dataset 116) associated withartifically intelligent entity 1102 a, or a personality bundle 1108.Once generated and linked to an artifically intelligent entity 1102 a,the portal token 1112 can be ingested by the computer system 124 togenerate the portal 1131. The portal 1131 can be generated so as toconform to the settings in the portal token 1112.

FIG. 13 shows a flowchart of an example of a method for improving theintelligence of a virtual ecosystem utilizing a centralized artificialintelligence engine according to some aspects of the present disclosure.Other examples may include more operations, fewer operations, differentoperations, or a different order of the operations than are shown inFIG. 13 . FIG. 13 may be performed by any of the systems describedherein, such as FIGS. 1, 3, and 11 .

In block 1302, a first artificially intelligent entity may generate aunique training dataset. It should be understood that this may mean thatan AI engine (e.g., AI engine 128) supporting the first artificiallyintelligent entity generates the unique training dataset. The uniquetraining dataset may be the result of an interaction between the firstartificially intelligent entity and another party, where the other partymay be a human user, a second artificially intelligent entity, or anysource of information (e.g., a database or real-time data source, suchas a stock ticker). In some examples, the interaction can occur in avirtual ecosystem such as the virtual ecosystem 130 in FIG. 1 . In otherexamples, the interaction may occur in a portal, such as the portal 1131in FIG. 11 .

In block 1304, the unique training dataset may be received by a computersystem that includes a centralized artificial intelligence engine. Insome examples, the computer system may be computer system 124 and thecentralized artifical intelligence engine may be AI engine 128. Thecentralized artificial intelligence engine may provide AI services(e.g., AI services 310) to any number of artificially intelligententities within the virtual ecosystem. In this way, the centralizedaritficial intelligence engine may be seen as providing intelligence tothe entire virtual ecosystem.

In some examples, the unique training dataset may be stored on thecomputer system. The unique training dataset may be made available toany artificially intelligent entity, or access may be restricted to onlythose artificailly intelligent entities which meet some predeterminedcriteria.

In block 1306, the unique training dataset may be used to train thecentralized artificial intelligence engine. For example, the uniquetraining dataset may be used in a supervised learning process or anunsupervised learning process to further tune the weights of theartifical intelligence engine. Through the training process, thecentralized artificial intelligence engine may be updated. This mayresult in an improved centralized intelligence engine.

In block 1308, a second artificially intelligent entity may perform atask using the improved centralized artificial intelligence engine. Thesecond artificial intelligence engine, therefore, uses the improvementsto the centralized artificial intelligence engine gained from the uniquetraining dataset generated by the first artificially intelligent entity.In some examples, the ouput of the task may be different when performedutilizing the improved centralized intelligence engine as compared tothe same task performed utilizing the centralized intelligence engine.

The improved artificial intelligence engine can provide AI services toany number of artificially intelligent entities within the virtualecosystem, causing the overall intelligence of the virtual ecosystem maybe improved. Because any number of artificially intelligent entities maybe active in the virtual ecosystem, any number of unique trainingdatasets may be generated. Each unique training dataset may be fed backinto the centralized artificial intelligence engine, allowing forimprovements and updates may be made on a substantially consistentbasis, increasing the overall intelligence of the virtual ecosystemrelatively continuously.

FIG. 14 shows a block diagram of an example of a computing device 1400usable to implement some aspects of the present disclosure. Thecomputing device 1400 may correspond to any of the client device 120,the computing node 104, or the computer system 124 described above. Forexample, the computing device 1400 may be part of the computer system124 and capable of performing any of the functionality described abovewith reference to the computer system 124.

The computing device 1400 can include a processor 1402 communicativelycoupled to a memory 1404. The processor 1402 can include one processingdevice or multiple processing devices. Non-limiting examples of theprocessor 1402 include a Field-Programmable Gate Array (FPGA), anapplication-specific integrated circuit (ASIC), a microprocessor, etc.The processor 1402 can execute program code 1406 stored in the memory1404 to perform operations. In some examples, the program code 1406 caninclude processor-specific instructions generated by a compiler or aninterpreter from code written in any suitable computer-programminglanguage, such as C, C++, C #, etc.

The memory 1404 can include one memory device or multiple memorydevices. The memory 1404 can be non-volatile and may include any type ofmemory device that retains stored information when powered off. Examplesof the memory 1404 include electrically erasable and programmableread-only memory (EEPROM), flash memory, or any other type ofnon-volatile memory. At least some of the memory 604 includes anon-transitory computer-readable medium from which the processor 1402can read program code 1406. A computer-readable medium can includeelectronic, optical, magnetic, or other storage devices capable ofproviding the processor 1402 with computer-readable instructions orother program code. Examples of a computer-readable medium includemagnetic disks, memory chips, ROM, random-access memory (RAM), an ASIC,a configured processor, optical storage, or any other medium from whicha computer processor can read the program code 1406.

The foregoing description of certain examples, including illustratedexamples, has been presented only for the purpose of illustration anddescription and is not intended to be exhaustive or to limit thedisclosure to the precise forms disclosed. Numerous modifications,adaptations, and uses thereof will be apparent to those skilled in theart without departing from the scope of the disclosure. For instance,any example(s) described herein can be combined with any otherexample(s) to yield further examples.

1. A non-transitory computer-readable medium comprising program codethat is executable by one or more processors for causing the one or moreprocessors to perform operations including: transmitting a first commandfor causing a non-fungible token (NFT) to be generated on a blockchain,the NFT representing an artificially intelligent entity; transmitting asecond command for causing a personality dataset to be stored on theblockchain, the personality dataset being stored on the blockchainseparately from the NFT and describing personality characteristics ofthe artificially intelligent entity; and transmitting a third command toexecute of a smart contract on the blockchain, the smart contract beingconfigured to correlate the NFT to the personality dataset and therebyassign the personality characteristics to the artificially intelligententity.
 2. The non-transitory computer-readable medium of claim 1,wherein the personality characteristics comprise intelligenceattributes, voice attributes, psyche attributes, identity attributes,and skill attributes.
 3. The non-transitory computer-readable medium ofclaim 2, wherein the intelligence attributes are adjustable over timeusing an artificial intelligence (AI) model.
 4. The non-transitorycomputer-readable medium of claim 3, wherein the AI model is acentralized AI model that is located off the blockchain, the centralizedAI model being configured to impart artificial intelligence to aplurality of artificially intelligent entities that are represented by aplurality of NFTs on the blockchain.
 5. The non-transitorycomputer-readable medium of claim 1, wherein the personality dataset isstored in one or more tokens on the blockchain, wherein the secondcommand is configured to cause the one or more tokens to be generated onthe blockchain, and wherein the smart contract is configured to generatea correlation between the NFT and the one or more tokens to assign thepersonality characteristics to the artificially intelligent entity. 6.The non-transitory computer-readable medium of claim 5, wherein thecorrelation is stored in a record that is separate from the NFT and theone or more tokens.
 7. The non-transitory computer-readable medium ofclaim 6, wherein the record is located on the blockchain.
 8. Thenon-transitory computer-readable medium of claim 6, wherein the recordis located off the blockchain.
 9. The non-transitory computer-readablemedium of claim 5, wherein the one or more tokens includes a pluralityof tokens.
 10. The non-transitory computer-readable medium of claim 1,wherein the personality dataset for the artificially intelligent entityis updatable based on tasks performed by the artificially intelligententity in a virtual environment.
 11. A method comprising: transmitting,by a processor, a first command for causing a non-fungible token (NFT)to be generated on a blockchain, the NFT representing an artificiallyintelligent entity; transmitting, by the processor, a second command forcausing a personality dataset to be stored on the blockchain, thepersonality dataset being stored on the blockchain separately from theNFT and describing personality characteristics of the artificiallyintelligent entity; and transmitting, by the processor, a third commandto execute of a smart contract on the blockchain, the smart contractbeing configured to correlate the NFT to the personality dataset andthereby assign the personality characteristics to the artificiallyintelligent entity.
 12. The method of claim 11, wherein the personalitycharacteristics comprise intelligence attributes, voice attributes, andpsyche attributes.
 13. The method of claim 12, wherein the intelligenceattributes are adjustable over time using an artificial intelligence(AI) model.
 14. The method of claim 13, wherein the AI model is acentralized AI model that is located off the blockchain, the centralizedAI model being configured to impart artificial intelligence to aplurality of artificially intelligent entities that are represented byNFTs on the blockchain.
 15. The method of claim 11, wherein thepersonality dataset is stored as one or more tokens on the blockchain,wherein the second command is configured to cause the one or more tokensto be generated on the blockchain, and wherein the smart contract isconfigured to generate a correlation between the NFT and the one or moretokens to assign the personality characteristics to the artificiallyintelligent entity.
 16. The method of claim 15, wherein the correlationis stored in a record that is separate from the NFT and the one or moretokens.
 17. The method of claim 16, wherein the record is located on theblockchain.
 18. The method of claim 11, wherein the personality datasetfor the artificially intelligent entity is updated based on tasksperformed by the artificially intelligent entity in a virtualenvironment.
 19. A method comprising: generating, by a processor, anon-fungible token (NFT) on a blockchain, the NFT representing anartificially intelligent entity; generating, by the processor, apersonality dataset on the blockchain, the personality dataset beingstored on the blockchain separately from the NFT and describingpersonality characteristics of the artificially intelligent entity; andexecuting, by the processor, a smart contract on the blockchain, thesmart contract being configured to correlate the NFT to the personalitydataset and thereby assign the personality characteristics to theartificially intelligent entity.
 20. The method of claim 19, wherein thepersonality characteristics comprise intelligence attributes, andwherein the intelligence attributes are adjustable over time using anartificial intelligence (AI) model.